Experiments settings: To evaluate the proposed method, we further provide a scribble annotation for the WORD dataset. We generate scribbles for all training volumes in the axial view. Note that the scribble annotations are very sparse in both intra-/inter-slices, which means that not all slices have the scribbles but each organ is annotated at least once in a volume. Due to the scribble annotations based abdominal multi-organ segmentation is not a hot research topic, there is no existing work or openly available codebase. We first build a benckmark for this task and then compare a widely-used method, CRFLoss [1]} on the WORD dataset. We use the ResUNet (2D) [2]} as our backbone and employ the nnUNet [3]} pipeline to train and test all methods. All implementations and scribble annotations will be released.
